System and method for diagnosing and monitoring congestive heart failure

ABSTRACT

A system for diagnosing and monitoring congestive heart failure for automated remote patient care is presented. A database stores a plurality of monitoring sets relating to patient information recorded on a substantially continuous basis. A server retrieving and processing the monitoring sets includes a comparison module determining patient status changes by comparing at least one recorded measure from one of the monitoring sets to at least one other recorded measure from another of the monitoring sets with both recorded measures relating to a type of patient information, and an analysis module testing each patient status change for one of an absence, an onset, a progression, a regression, and a status quo of congestive heart failure against a predetermined indicator threshold corresponding to a type of patient information as the recorded measures. The indicator threshold corresponds to a quantifiable physiological measure of a pathophysiology indicative of congestive heart failure.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 10/646,105, filed Aug. 22, 2003, pending; which is acontinuation of U.S. Pat. No. 6,811,537, issued Nov. 2, 2004; which is acontinuation of U.S. Pat. No. 6,336,903, issued Jan. 8, 2002, thepriority of filing dates of which are claimed, and the disclosures ofwhich are incorporated by reference.

FIELD OF THE INVENTION

The present invention relates in general to congestive heart failure(CHF) diagnosis and analysis, and, in particular, to an automatedcollection and analysis patient care system and method for diagnosingand monitoring congestive heart failure and outcomes thereof throughoutdisease onset, progression, regression, and status quo.

BACKGROUND OF THE INVENTION

Presently, congestive heart failure is one of the leading causes ofcardiovascular disease-related deaths in the world. Clinically,congestive heart failure involves circulatory congestion caused by heartdisorders that are primarily characterized by abnormalities of leftventricular function and neurohormonal regulation. Congestive heartfailure occurs when these abnormalities cause the heart to fail to pumpblood at a rate required by the metabolizing tissues. The effects ofcongestive heart failure range from impairment during physical exertionto a complete failure of cardiac pumping function at any level ofactivity. Clinical manifestations of congestive heart failure includerespiratory distress, such as shortness of breath and fatigue, andreduced exercise capacity or tolerance.

Several factors make the early diagnosis and prevention of congestiveheart failure, as well as the monitoring of the progression ofcongestive heart failure, relatively difficult. First, the onset ofcongestive heart failure is generally subtle and erratic. Often, thesymptoms are ignored and the patient compensates by changing his or herdaily activities. As a result, many congestive heart failure conditionsor deteriorations in congestive heart failure remain undiagnosed untilmore serious problems arise, such as pulmonary edema or cardiac arrest.Moreover, the susceptibility to suffer from congestive heart failuredepends upon the patient's age, sex, physical condition, and otherfactors, such as diabetes, lung disease, high blood pressure, and kidneyfunction. No one factor is dispositive. Finally, annual or even monthlycheckups provide, at best, a “snapshot” of patient wellness and theincremental and subtle clinicophysiological changes which portend theonset or progression of congestive heart failure often go unnoticed,even with regular health care. Documentation of subtle improvementsfollowing therapy, that can guide and refine further evaluation andtherapy, can be equally elusive.

Nevertheless, taking advantage of frequently and regularly measuredphysiological measures, such as recorded manually by a patient, via anexternal monitoring or therapeutic device, or via implantable devicetechnologies, can provide a degree of detection and preventionheretofore unknown. For instance, patients already suffering from someform of treatable heart disease often receive an implantable pulsegenerator (IPG), cardiovascular or heart failure monitor, therapeuticdevice, or similar external wearable device, with which rhythm andstructural problems of the heart can be monitored and treated. Thesetypes of devices are useful for detecting physiological changes inpatient conditions through the retrieval and analysis of telemeteredsignals stored in an on-board, volatile memory. Typically, these devicescan store more than thirty minutes of per heartbeat data recorded on aper heartbeat, binned average basis, or on a derived basis from, forexample, atrial or ventricular electrical activity, minute ventilation,patient activity score, cardiac output score, mixed venous oxygen score,cardiovascular pressure measures, and the like. However, the properanalysis of retrieved telemetered signals requires detailed medicalsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists.

Alternatively, these telemetered signals can be remotely collected andanalyzed using an automated patient care system. One such system isdescribed in a related, commonly assigned U.S. Pat. No. 6,312,378,issued Nov. 6, 2001, the disclosure of which is incorporated herein byreference. A medical device adapted to be implanted in an individualpatient records telemetered signals that are then retrieved on aregular, periodic basis using an interrogator or similar interfacingdevice. The telemetered signals are downloaded via an internetwork ontoa network server on a regular, e.g., daily, basis and stored as sets ofcollected measures in a database along with other patient care records.The information is then analyzed in an automated fashion and feedback,which includes a patient status indicator, is provided to the patient.

While such an automated system can serve as a valuable tool in providingremote patient care, an approach to systematically correlating andanalyzing the raw collected telemetered signals, as well as manuallycollected physiological measures, through applied cardiovascular medicalknowledge to accurately diagnose the onset of a particular medicalcondition, such as congestive heart failure, is needed. One automatedpatient care system directed to a patient-specific monitoring functionis described in U.S. Pat. No. 5,113,869 ('869) to Nappholz et al. The'869 patent discloses an implantable, programmable electrocardiography(ECG) patient monitoring device that senses and analyzes ECG signals todetect ECG and physiological signal characteristics predictive ofmalignant cardiac arrhythmias. The monitoring device can communicate awarning signal to an external device when arrhythmias are predicted.However, the Nappholz device is limited to detecting tachycardias.Unlike requirements for automated congestive heart failure monitoring,the Nappholz device focuses on rudimentary ECG signals indicative ofmalignant cardiac tachycardias, an already well established techniquethat can be readily used with on-board signal detection techniques.Also, the Nappholz device is patient specific only and is unable toautomatically take into consideration a broader patient or peer grouphistory for reference to detect and consider the progression orimprovement of cardiovascular disease. Moreover, the Nappholz device hasa limited capability to automatically self-reference multiple datapoints in time and cannot detect disease regression even in theindividual patient. Also, the Nappholz device must be implanted andcannot function as an external monitor. Finally, the Nappholz device isincapable of tracking the cardiovascular and cardiopulmonaryconsequences of any rhythm disorder.

Consequently, there is a need for a systematic approach to detectingtrends in regularly collected physiological data indicative of theonset, progression, regression, or status quo of congestive heartfailure diagnosed and monitored using an automated, remote patient caresystem. The physiological data could be telemetered signals datarecorded either by an external or an implantable medical device or,alternatively, individual measures collected through manual means.Preferably, such an approach would be capable of diagnosing both acuteand chronic congestive heart failure conditions, as well as the symptomsof other cardiovascular diseases. In addition, findings from individual,peer group, and general population patient care records could beintegrated into continuous, on-going monitoring and analysis.

SUMMARY OF THE INVENTION

The present invention provides a system and method for diagnosing andmonitoring the onset, progression, regression, and status quo ofcongestive heart failure using an automated collection and analysispatient care system. Measures of patient cardiovascular information areeither recorded by an external or implantable medical device, such as anIPG, cardiovascular or heart failure monitor, or therapeutic device, ormanually through conventional patient-operable means. The measures arecollected on a regular, periodic basis for storage in a database alongwith other patient care records. Derived measures are developed from thestored measures. Select stored and derived measures are analyzed andchanges in patient condition are logged. The logged changes are comparedto quantified indicator thresholds to detect findings of respiratorydistress or reduced exercise capacity indicative of the two principalcardiovascular pathophysiological manifestations of congestive heartfailure: elevated left ventricular end diastolic pressure and reducedcardiac output, respectively.

An embodiment of the present invention is an automated system and methodfor diagnosing and monitoring congestive heart failure and outcomesthereof. A plurality of monitoring sets is retrieved from a database.Each of the monitoring sets includes recorded measures relating topatient information recorded on a substantially continuous basis. Apatient status change is determined by comparing at least one recordedmeasure from each of the monitoring sets to at least one other recordedmeasure. Both recorded measures relate to the same type of patientinformation. Each patient status change is tested against an indicatorthreshold corresponding to the same type of patient information as therecorded measures that were compared. The indicator thresholdcorresponds to a quantifiable physiological measure of a pathophysiologyindicative of congestive heart failure.

A further embodiment is an automated collection and analysis patientcare system and method for diagnosing and monitoring congestive heartfailure and outcomes thereof. A plurality of monitoring sets isretrieved from a database. Each monitoring set includes recordedmeasures that each relates to patient information and include eithermedical device measures or derived measures calculable therefrom. Themedical device measures are recorded on a substantially continuousbasis. A set of indicator thresholds is defined. Each indicatorthreshold corresponds to a quantifiable physiological measure of apathophysiology indicative of congestive heart failure and relates tothe same type of patient information as at least one of the recordedmeasures. A congestive heart failure finding is diagnosed. A change inpatient status is determined by comparing at least one recorded measureto at least one other recorded measure with both recorded measuresrelating to the same type of patient information. Each patient statuschange is compared to the indicator threshold corresponding to the sametype of patient information as the recorded measures that were compared.

A further embodiment is an automated patient care system and method fordiagnosing and monitoring congestive heart failure and outcomes thereof.Recorded measures organized into a monitoring set for an individualpatient are stored into a database. Each recorded measure is recorded ona substantially continuous basis and relates to at least one aspect ofmonitoring reduced exercise capacity and/or respiratory distress. Aplurality of the monitoring sets is periodically retrieved from thedatabase. At least one measure related to congestive heart failureonset, progression, regression, and status quo is evaluated. A patientstatus change is determined by comparing at least one recorded measurefrom each of the monitoring sets to at least one other recorded measurewith both recorded measures relating to the same type of patientinformation. Each patient status change is tested against an indicatorthreshold corresponding to the same type of patient information as therecorded measures that were compared. The indicator thresholdcorresponds to a quantifiable physiological measure of a pathophysiologyindicative of reduced exercise capacity and/or respiratory distress.

The present invention provides a capability to detect and track subtletrends and incremental changes in recorded patient information fordiagnosing and monitoring congestive heart failure. When coupled with anenrollment in a remote patient monitoring service having the capabilityto remotely and continuously collect and analyze external or implantablemedical device measures, congestive heart failure detection, prevention,and tracking regression from therapeutic maneuvers become feasible.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an automated collection and analysispatient care system for diagnosing and monitoring congestive heartfailure and outcomes thereof in accordance with the present invention;

FIG. 2 is a database schema showing, by way of example, the organizationof a device and derived measures set record for care of patients withcongestive heart failure stored as part of a patient care record in thedatabase of the system of FIG. 1;

FIG. 3 is a database schema showing, by way of example, the organizationof a quality of life and symptom measures set record for care ofpatients with congestive heart failure stored as part of a patient carerecord in the database of the system of FIG. 1;

FIG. 4 is a database schema showing, by way of example, the organizationof a combined measures set record for care of patients with congestiveheart failure stored as part of a patient care record in the database ofthe system of FIG. 1;

FIG. 5 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialpatient care records for care of patients with congestive heart failurestored in the database of the system of FIG. 1;

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records of FIG. 6;

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring congestive heart failure and outcomes thereof using anautomated collection and analysis patient care system in accordance withthe present invention;

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets for use in the method of FIGS. 8A-8B;

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets for use in the method of FIGS. 8A-8B;

FIGS. 11A-11D are flow diagrams showing the routine for testingthreshold limits for use in the method of FIGS. 8A-8B;

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression, and status quo of congestive heart failure foruse in the method of FIGS. 8A-8B;

FIGS. 13A-13B are flow diagrams showing the routine for determining anonset of congestive heart failure for use in the routine of FIG. 12;

FIGS. 14A-14B are flow diagrams showing the routine for determiningprogression or worsening of congestive heart failure for use in theroutine of FIG. 12;

FIGS. 15A-15B are flow diagrams showing the routine for determiningregression or improving of congestive heart failure for use in theroutine of FIG. 12; and

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) for use in the method of FIG. 12.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing an automated collection and analysispatient care system 10 for diagnosing and monitoring congestive heartfailure in accordance with the present invention. An exemplary automatedcollection and analysis patient care system suitable for use with thepresent invention is disclosed in the related, commonly assigned U.S.Pat. No. 6,312,378, issued Nov. 6, 2001, the disclosure of which isincorporated herein by reference. Preferably, an individual patient 11is a recipient of an implantable medical device 12, such as, by way ofexample, an IPG, cardiovascular or heart failure monitor, or therapeuticdevice, with a set of leads extending into his or her heart andelectrodes implanted throughout the cardiopulmonary system.Alternatively, an external monitoring or therapeutic medical device 26,a subcutaneous monitor or device inserted into other organs, a cutaneousmonitor, or even a manual physiological measurement device, such as anelectrocardiogram or heart rate monitor, could be used. The implantablemedical device 12 and external medical device 26 include circuitry forrecording into a short-term, volatile memory telemetered signals storedfor later retrieval, which become part of a set of device and derivedmeasures, such as described below, by way of example, with reference toFIG. 2. Exemplary implantable medical devices suitable for use in thepresent invention include the Discovery line of pacemakers, manufacturedby Guidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

The telemetered signals stored in the implantable medical device 12 arepreferably retrieved upon the completion of an initial observationperiod and subsequently thereafter on a continuous, periodic (daily)basis, such as described in the related, commonly assigned U.S. Pat. No.6,221,011, issued Apr. 24, 2001, the disclosure of which is incorporatedherein by reference. A programmer 14, personal computer 18, or similardevice for communicating with an implantable medical device 12 can beused to retrieve the telemetered signals. A magnetized reed switch (notshown) within the implantable medical device 12 closes in response tothe placement of a wand 13 over the site of the implantable medicaldevice 12. The programmer 14 sends programming or interrogatinginstructions to and retrieves stored telemetered signals from theimplantable medical device 12 via RF signals exchanged through the wand13. Similar communication means are used for accessing the externalmedical device 26. Once downloaded, the telemetered signals are sent viaan internetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals as devicemeasures in patient care records 23 in a database 17, as furtherdescribed below, by way of example, with reference to FIGS. 2 and 3. Anexemplary programmer 14 suitable for use in the present invention is theModel 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind.

The patient 11 is remotely monitored by the server system 16 via theinternetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The device measures sets areperiodically analyzed and compared by the server system 16 to indicatorthresholds corresponding to quantifiable physiological measures of apathophysiology indicative of congestive heart failure, as furtherdescribed below with reference to FIG. 5. As necessary, feedback isprovided to the patient 11. By way of example, the feedback includes anelectronic mail message automatically sent by the server system 16 overthe internetwork 15 to a personal computer 18 (PC) situated for localaccess by the patient 11. Alternatively, the feedback can be sentthrough a telephone interface device 19 as an automated voice mailmessage to a telephone 21 or as an automated facsimile message to afacsimile machine 22, both also situated for local access by the patient11. Moreover, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider 29using similar feedback means to deliver the information.

The server system 10 can consist of either a single computer system or acooperatively networked or clustered set of computer systems. Eachcomputer system is a general purpose, programmed digital computingdevice consisting of a central processing unit (CPU), random accessmemory (RAM), non-volatile secondary storage, such as a hard drive or CDROM drive, network interfaces, and peripheral devices, including userinterfacing means, such as a keyboard and display. Program code,including software programs, and data are loaded into the RAM forexecution and processing by the CPU and results are generated fordisplay, output, transmittal, or storage, as is known in the art.

The database 17 stores patient care records 23 for each individualpatient to whom remote patient care is being provided. Each patient carerecord 23 contains normal patient identification and treatment profileinformation, as well as medical history, medications taken, height andweight, and other pertinent data (not shown). The patient care records23 consist primarily of two sets of data: device and derived measures(D&DM) sets 24 a, 24 b and quality of life (QOL) sets 25 a, 25 b, theorganization of which are further described below with respect to FIGS.2 and 3, respectively. The device and derived measures sets 24 a, 24 band quality of life and symptom measures sets 25 a, 25 b can be furtherlogically categorized into two potentially overlapping sets. Thereference baseline 26 is a special set of device and derived referencemeasures sets 24 a and quality of life and symptom measures sets 25 arecorded and determined during an initial observation period. Monitoringsets 27 are device and derived measures sets 24 b and quality of lifeand symptom measures sets 25 b recorded and determined thereafter on aregular, continuous basis. Other forms of database organization arefeasible.

The implantable medical device 12 and, in a more limited fashion, theexternal medical device 26, record patient information for care ofpatients with congestive heart failure on a regular basis. The recordedpatient information is downloaded and stored in the database 17 as partof a patient care record 23. Further patient information can be derivedfrom recorded data, as is known in the art. FIG. 2 is a database schemashowing, by way of example, the organization of a device and derivedmeasures set record 40 for patient care stored as part of a patient carerecord in the database 17 of the system of FIG. 1. Each record 40 storespatient information which includes a snapshot of telemetered signalsdata which were recorded by the implantable medical device 12 or theexternal medical device 26, for instance, on per heartbeat, binnedaverage or derived bases; measures derived from the recorded devicemeasures; and manually collected information, such as obtained through apatient medical history interview or questionnaire. The followingnon-exclusive information can be recorded for a patient: atrialelectrical activity 41, ventricular electrical activity 42, PR intervalor AV interval 43, QRS measures 44, ST-T wave measures 45, QT interval46, body temperature 47, patient activity score 48, posture 49,cardiovascular pressures 50, pulmonary artery diastolic pressure measure51, cardiac output 52, systemic blood pressure 53, patient geographiclocation (altitude) 54, mixed venous oxygen score 55, arterial oxygenscore 56, pulmonary measures 57, minute ventilation 58, potassium [K+]level 59, sodium [Na+] level 60, glucose level 61, blood urea nitrogen(BUN) and creatinine 62, acidity (pH) level 63, hematocrit 64, hormonallevels 65, cardiac injury chemical tests 66, myocardial blood flow 67,central nervous system (CNS) injury chemical tests 68, central nervoussystem blood flow 69, interventions made by the implantable medicaldevice or external medical device 70, and the relative success of anyinterventions made 71. In addition, the implantable medical device orexternal medical device communicates device-specific information,including battery status, general device status and program settings 72and the time of day 73 for the various recorded measures. Other types ofcollected, recorded, combined, or derived measures are possible, as isknown in the art.

The device and derived measures sets 24 a, 24 b (shown in FIG. 1), alongwith quality of life and symptom measures sets 25 a, 25 b, as furtherdescribed below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly assigned U.S. Pat. No. 6,221,011, issued Apr. 24,2001, the disclosure of which is incorporated herein by reference.

As an adjunct to remote patient care through the monitoring of measuredphysiological data via the implantable medical device 12 or externalmedical device 26, quality of life and symptom measures sets 25 a canalso be stored in the database 17 as part of the reference baseline 26,if used, and the monitoring sets 27. A quality of life measure is asemi-quantitative self-assessment of an individual patient's physicaland emotional well being and a record of symptoms, such as provided bythe Duke Activities Status Indicator. These scoring systems can beprovided for use by the patient 11 on the personal computer 18 (shown inFIG. 1) to record his or her quality of life scores for both initial andperiodic download to the server system 16. FIG. 3 is a database schemashowing, by way of example, the organization of a quality of life record80 for use in the database 17. The following information is recorded fora patient: overall health wellness 81, psychological state 82,activities of daily living 83, work status 84, geographic location 85,family status 86, shortness of breath 87, energy level 88, exercisetolerance 89, chest discomfort 90, time of day 91, and other quality oflife and symptom measures as would be known to one skilled in the art.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W.B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

On a periodic basis, the patient information stored in the database 17is analyzed and compared to pre-determined cutoff levels, which, whenexceeded, can provide etiological indications of congestive heartfailure symptoms. FIG. 4 is a database schema showing, by way ofexample, the organization of a combined measures set record 95 for usein the database 17. Each record 95 stores patient information obtainedor derived from the device and derived measures sets 24 a, 24 b andquality of life and symptom measures sets 25 a, 25 b as maintained inthe reference baseline 26, if used, and the monitoring sets 27. Thecombined measures set 95 represents those measures most (but notexhaustively or exclusively) relevant to a pathophysiology indicative ofcongestive heart failure and are determined as further described belowwith reference to FIGS. 8A-8B. The following information is stored for apatient: heart rate 96, heart rhythm (e.g., normal sinus vs. atrialfibrillation) 97, pacing modality 98, pulmonary artery diastolicpressure 99, cardiac output 100, arterial oxygen score 101, mixed venousoxygen score 102, respiratory rate 103, transthoracic impedance 104,patient activity score 105, posture 106, exercise tolerance quality oflife and symptom measures 107, respiratory distress quality of life andsymptom measures 108, any interventions made to treat congestive heartfailure 109, including treatment by medical device, via drug infusionadministered by the patient or by a medical device, surgery, and anyother form of medical intervention as is known in the art, the relativesuccess of any such interventions made 110, and time of day 111. Othertypes of comparison measures regarding congestive heart failure arepossible as is known in the art. In the described embodiment, eachcombined measures set 95 is sequentially retrieved from the database 17and processed. Alternatively, each combined measures set 95 could bestored within a dynamic data structure maintained transitorily in therandom access memory of the server system 16 during the analysis andcomparison operations.

FIG. 5 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU of the server system 16 as object or byte code, as is known inthe art. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium or embodiedon a transmission medium in a carrier wave. The server system 16includes three primary software modules, database module 125, diagnosticmodule 126, and feedback module 128, which perform integrated functionsas follows.

First, the database module 125 organizes the individual patient carerecords 23 stored in the database 17 (shown in FIG. 1) and efficientlystores and accesses the reference baseline 26, monitoring sets 27, andpatient care data maintained in those records. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

Next, the diagnostic module 126 makes findings of congestive heartfailure based on the comparison and analysis of the data measures fromthe reference baseline 26 and monitoring sets 27. The diagnostic moduleincludes three modules: comparison module 130, analysis module 131, andquality of life module 132. The comparison module 130 compares recordedand derived measures retrieved from the reference baseline 26, if used,and monitoring sets 27 to indicator thresholds 129. The database 17stores individual patient care records 23 for patients suffering fromvarious health disorders and diseases for which they are receivingremote patient care. For purposes of comparison and analysis by thecomparison module 130, these records can be categorized into peer groupscontaining the records for those patients suffering from similardisorders, as well as being viewed in reference to the overall patientpopulation. The definition of the peer group can be progressivelyrefined as the overall patient population grows. To illustrate, FIG. 6is a record view showing, by way of example, a set of partial patientcare records for care of patients with congestive heart failure storedin the database 17 for three patients, Patient 1, Patient 2, and Patient3. For each patient, three sets of peer measures, X, Y, and Z, areshown. Each of the measures, X, Y, and Z, could be either collected orderived measures from the reference baseline 26, if used, and monitoringsets 27.

The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀ through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined over time as the number of monitored patients grows. Measuresrepresenting different types of patient information, such as measuresX₀, Y₀, and Z₀, are sibling measures. These are measures which are alsomeasured over time, but which might have medically significant meaningwhen compared to each other within a set for an individual patient.

The comparison module 130 performs two basic forms of comparison. First,individual measures for a given patient can be compared to otherindividual measures for that same patient (self-referencing). Thesecomparisons might be peer-to-peer measures, that is, measures relatingto a one specific type of patient information, projected over time, forinstance, X_(n), X_(n−1), X_(n−2), . . . X₀, or sibling-to-siblingmeasures, that is, measures relating to multiple types of patientinformation measured during the same time period, for a single snapshot,for instance, X_(n), Y_(n), and Z_(n), or projected over time, forinstance, X_(n), Y_(n), Z_(n), X_(n−1), Y_(n−1), Z_(n−1), X_(n−2),Y_(n−2), Z_(n−2), . . . X₀, Y₀, Z₀. Second, individual measures for agiven patient can be compared to other individual measures for a groupof other patients sharing the same disorder- or disease-specificcharacteristics (peer group referencing) or to the patient population ingeneral (population referencing). Again, these comparisons might bepeer-to-peer measures projected over time, for instance, X_(n), X_(n′),X_(n″), X_(n−1), X_(n−1′), X_(n−1″), X_(n−2), X_(n−2′), X_(n−2″) . . .X₀, X_(0′), X_(0″), or comparing the individual patient's measures to anaverage from the group. Similarly, these comparisons might besibling-to-sibling measures for single snapshots, for instance, X_(n),X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), and Z_(n), Z_(n′), Z_(n″), orprojected over time, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′),Y_(n″), Z_(n), Z_(n′), Z_(n″), X_(n−1), X_(n−1′), X_(n−1″), Y_(n−1),Y_(n−1′), Y_(n−1″), Z_(n−1), Z_(n−1′), Z_(n−1″), X_(n−2), X_(n−2′),X_(n−2″), Y_(n−2), Y_(n−2′), Y_(n−2″), Z_(n−2), Z_(n−2′), Z_(n−2″) . . .X₀, X_(0′), X_(0″), Y₀, Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Otherforms of comparisons are feasible, including multiple disease diagnosesfor diseases exhibiting similar abnormalities in physiological measuresthat might result from a second disease but manifest in differentcombinations or onset in different temporal sequences.

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 23 of FIG. 1. Each patient carerecord 23 includes characteristics data 350, 351, 352, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

Referring back to FIG. 5, the analysis module 131 analyzes the resultsfrom the comparison module 130, which are stored as a combined measuresset 95 (not shown), to a set of indicator thresholds 129, as furtherdescribed below with reference to FIGS. 8A-8B. Similarly, the quality oflife module 132 compares quality of life and symptom measures set 25 a,25 b from the reference baseline 26 and monitoring sets 27, the resultsof which are incorporated into the comparisons performed by the analysismodule 131, in part, to either refute or support the findings based onphysiological “hard” data. Finally, the feedback module 128 providesautomated feedback to the individual patient based, in part, on thepatient status indicator 127 generated by the diagnostic module 126. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related, commonlyassigned U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, the disclosureof which is incorporated herein by reference. In addition, the feedbackmodule 128 determines whether any changes to interventive measures areappropriate based on threshold stickiness (“hysteresis”) 133, as furtherdescribed below with reference to FIG. 16. The threshold stickiness 133can prevent fickleness in diagnostic routines resulting from transient,non-trending and non-significant fluctuations in the various collectedand derived measures in favor of more certainty in diagnosis. In afurther embodiment of the present invention, the feedback module 128includes a patient query engine 134 which enables the individual patient11 to interactively query the server system 16 regarding the diagnosis,therapeutic maneuvers, and treatment regimen. Conversely, the patientquery engines 134, found in interactive expert systems for diagnosingmedical conditions, can interactively query the patient. Using thepersonal computer 18 (shown in FIG. 1), the patient can have aninteractive dialogue with the automated server system 16, as well ashuman experts as necessary, to self assess his or her medical condition.Such expert systems are well known in the art, an example of which isthe MYCIN expert system developed at Stanford University and describedin Buchanan, B. & Shortlife, E., “RULE-BASED EXPERT SYSTEMS. The MYCINExperiments of the Stanford Heuristic Programming Project,”Addison-Wesley (1984). The various forms of feedback described abovehelp to increase the accuracy and specificity of the reporting of thequality of life and symptomatic measures.

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring congestive heart failure and outcomes thereof 135 using anautomated collection and analysis patient care system 10 in accordancewith the present invention. First, the indicator thresholds 129 (shownin FIG. 5) are set (block 136) by defining a quantifiable physiologicalmeasure of a pathophysiology indicative of congestive heart failure andrelating to the each type of patient information in the combined deviceand derived measures set 95 (shown in FIG. 4). The actual values of eachindicator threshold can be finite cutoff values, weighted values, orstatistical ranges, as discussed below with reference to FIGS. 11A-11D.Next, the reference baseline 26 (block 137) and monitoring sets 27(block 138) are retrieved from the database 17, as further describedbelow with reference to FIGS. 9 and 10, respectively. Each measure inthe combined device and derived measures set 95 is tested against thethreshold limits defined for each indicator threshold 129 (block 139),as further described below with reference to FIGS. 11A-11D. Thepotential onset, progression, regression, or status quo of congestiveheart failure is then evaluated (block 140) based upon the findings ofthe threshold limits tests (block 139), as further described below withreference to FIGS. 13A-13B, 14A-14B, 15A-15B.

In a further embodiment, multiple near-simultaneous disorders areconsidered in addition to primary congestive heart failure. Primarycongestive heart failure is defined as the onset or progression ofcongestive heart failure without obvious inciting cause. Secondarycongestive heart failure is defined as the onset or progression ofcongestive heart failure (in a patient with or without pre-existingcongestive heart failure) from another disease process, such as coronaryinsufficiency, respiratory insufficiency, atrial fibrillation, and soforth. Other health disorders and diseases can potentially share thesame forms of symptomatology as congestive heart failure, such asmyocardial ischemia, respiratory insufficiency, pneumonia, exacerbationof chronic bronchitis, renal failure, sleep-apnea, stroke, anemia,atrial fibrillation, other cardiac arrhythmias, and so forth. If morethan one abnormality is present, the relative sequence and magnitude ofonset of abnormalities in the monitored measures becomes most importantin sorting and prioritizing disease diagnosis and treatment.

Thus, if other disorders or diseases are being cross-referenced anddiagnosed (block 141), their status is determined (block 142). In thedescribed embodiment, the operations of ordering and prioritizingmultiple near-simultaneous disorders (box 151) by the testing ofthreshold limits and analysis in a manner similar to congestive heartfailure as described above, preferably in parallel to the presentdetermination, is described in the related, commonly assigned U.S. Pat.No. 6,440,066, entitled “Automated Collection And Analysis Patient CareSystem And Method For Ordering And Prioritizing Multiple HealthDisorders To Identify An Index Disorder,” issued Aug. 27, 2002, thedisclosure of which is incorporated herein by reference. If congestiveheart failure is due to an obvious inciting cause, i.e., secondarycongestive heart failure, (block 143), an appropriate treatment regimenfor congestive heart failure as exacerbated by other disorders isadopted that includes treatment of secondary disorders, e.g., myocardialischemia, respiratory insufficiency, atrial fibrillation, and so forth(block 144) and a suitable patient status indicator 127 for congestiveheart failure is provided (block 146) to the patient. Suitable devicesand approaches to diagnosing and treating myocardial infarction,respiratory distress and atrial fibrillation are described in related,commonly-assigned U.S. Pat. No. 6,368,284, entitled “AutomatedCollection And Analysis Patient Care System And Method For DiagnosingAnd Monitoring Myocardial Ischemia And Outcomes Thereof,” issued Apr. 9,2002; U.S. Pat. No. 6,398,728, entitled “Automated Collection AndAnalysis Patient Care System And Method For Diagnosing And MonitoringRespiratory Insufficiency And Outcomes Thereof,” issued Jun. 4, 2002;and U.S. Pat. No. 6,411,840, entitled “Automated Collection And AnalysisPatient Care System And Method For Diagnosing And Monitoring TheOutcomes Of Atrial Fibrillation” issued Jun. 25, 2002, the disclosuresof which are incorporated herein by reference.

Otherwise, if primary congestive heart failure is indicated (block 143),a primary treatment regimen is followed (block 145). A patient statusindicator 127 for congestive heart failure is provided (block 146) tothe patient regarding physical well-being, disease prognosis, includingany determinations of disease onset, progression, regression, or statusquo, and other pertinent medical and general information of potentialinterest to the patient.

Finally, in a further embodiment, if the patient submits a query to theserver system 16 (block 147), the patient query is interactivelyprocessed by the patient query engine (block 148). Similarly, if theserver elects to query the patient (block 149), the server query isinteractively processed by the server query engine (block 150). Themethod then terminates if no further patient or server queries aresubmitted.

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets 137 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate reference baseline sets 26,if used, from the database 17 based on the types of comparisons beingperformed. First, if the comparisons are self referencing with respectto the measures stored in the individual patient care record 23 (block152), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfor the individual patient from the database 17 (block 153). Next, ifthe comparisons are peer group referencing with respect to measuresstored in the patient care records 23 for a health disorder- ordisease-specific peer group (block 154), the reference device andderived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 155). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation (SD), andtrending data) from the reference baseline 26 for the peer group is thencalculated (block 156). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 157), the reference deviceand derived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 from the database 17 (block 158). Minimum, maximum, averaged,standard deviation, and trending data and other numerical processesusing the data, as is known in the art, for each measure from thereference baseline 26 for the peer group is then calculated (block 159).The routine then returns.

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets 138 for use in the method of FIGS. 8A-8B. The purpose of thisroutine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 160), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 161). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 162),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 163). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation, andtrending data) from the monitoring sets 27 for the peer group is thencalculated (block 164). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 165), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 166). Minimum, maximum, averaged, standard deviation,and trending data and other numerical processes using the data, as isknown in the art, for each measure from the monitoring sets 27 for thepeer group is then calculated (block 167). The routine then returns.

FIGS. 11A-11D are flow diagrams showing the routine for testingthreshold limits 139 for use in the method of FIGS. 8A and 8B. Thepurpose of this routine is to analyze, compare, and log any differencesbetween the observed, objective measures stored in the referencebaseline 26, if used, and the monitoring sets 27 to the indicatorthresholds 129. Briefly, the routine consists of tests pertaining toeach of the indicators relevant to diagnosing and monitoring congestiveheart failure. The threshold tests focus primarily on: (1) changes toand rates of change for the indicators themselves, as stored in thecombined device and derived measures set 95 (shown in FIG. 4) or similardata structure; and (2) violations of absolute threshold limits whichtrigger an alert. The timing and degree of change may vary with eachmeasure and with the natural fluctuations noted in that measure duringthe reference baseline period. In addition, the timing and degree ofchange might also vary with the individual and the natural history of ameasure for that patient.

One suitable approach to performing the threshold tests uses a standardstatistical linear regression technique using a least squares error fit.The least squares error fit can be calculated as follows:$\begin{matrix}{y = {\beta_{0} + {\beta_{1}x}}} & (1) \\{\beta = \frac{{SS}_{xy}}{{SS}_{xx}}} & (2) \\{{SS}_{xy} = {{\sum\limits_{i = 1}^{n}{x_{i}y_{i}}} - \frac{\left( {\sum\limits_{i = 1}^{n}x_{i}} \right)\left( {\sum\limits_{i = 1}^{n}y_{i}} \right)}{n}}} & (3) \\{{SS}_{xx} = {{\sum\limits_{i = 1}^{n}x_{i}^{2}} - \frac{\left( {\sum\limits_{i = 1}^{n}x_{i}} \right)^{2}}{n}}} & (4)\end{matrix}$where n is the total number of measures, x_(i) is the time of day formeasure i, and y_(i) is the value of measure i, β_(i) is the slope, andβ₀ is the y-intercept of the least squares error line. A positive slopeβ₁ indicates an increasing trend, a negative slope β₁ indicates adecreasing trend, and no slope indicates no change in patient conditionfor that particular measure. A predicted measure value can be calculatedand compared to the appropriate indicator threshold 129 for determiningwhether the particular measure has either exceeded an acceptablethreshold rate of change or the absolute threshold limit.

For any given patient, three basic types of comparisons betweenindividual measures stored in the monitoring sets 27 are possible: selfreferencing, peer group, and general population, as explained above withreference to FIG. 6. In addition, each of these comparisons can includecomparisons to individual measures stored in the pertinent referencebaselines 24.

The indicator thresholds 129 for detecting a trend indicatingprogression into a state of congestive heart failure or a state ofimminent or likely congestive heart failure, for example, over a oneweek time period, can be as follows:

-   -   (1) Respiratory rate (block 170): If the respiratory rate has        increased over 1.0 SD from the mean respiratory rate in the        reference baseline 26 (block 171), the increased respiratory        rate and time span over which it occurs are logged in the        combined measures set 95 (block 172).    -   (2) Heart rate (block 173): If the heart rate has increased over        1.0 SD from the mean heart rate in the reference baseline 26        (block 174), the increased heart rate and time span over which        it occurs are logged in the combined measures set 95 (block        175).    -   (3) Pulmonary artery diastolic pressure (PADP) (block 176)        reflects left ventricular filling pressure and is a measure of        left ventricular dysfunction. Ideally, the left ventricular end        diastolic pressure (LVEDP) should be monitored, but in practice        is difficult to measure. Consequently, without the LVEDP, the        PADP, or derivatives thereof, is suitable for use as an        alternative to LVEDP in the present invention. If the PADP has        increased over 1.0 SD from the mean PADP in the reference        baseline 26 (block 177), the increased PADP and time span over        which that increase occurs, are logged in the combined measures        set 95 (block 178). Other cardiac pressures or derivatives could        also apply.    -   (4) Transthoracic impedance (block 179): If the transthoracic        impedance has decreased over 1.0 SD from the mean transthoracic        impedance in the reference baseline 26 (block 180), the        decreased transthoracic impedance and time span are logged in        the combined measures set 95 (block 181).    -   (5) Arterial oxygen score (block 182): If the arterial oxygen        score has decreased over 1.0 SD from the arterial oxygen score        in the reference baseline 26 (block 183), the decreased arterial        oxygen score and time span are logged in the combined measures        set 95 (block 184).    -   (6) Venous oxygen score (block 185): If the venous oxygen score        has decreased over 1.0 SD from the mean venous oxygen score in        the reference baseline 26 (block 186), the decreased venous        oxygen score and time span are logged in the combined measures        set 95 (block 187).    -   (7) Cardiac output (block 188): If the cardiac output has        decreased over 1.0 SD from the mean cardiac output in the        reference baseline 26 (block 189), the decreased cardiac output        and time span are logged in the combined measures set 95 (block        190).    -   (8) Patient activity score (block 191): If the mean patient        activity score has decreased over 1.0 SD from the mean patient        activity score in the reference baseline 26 (block 192), the        decreased patient activity score and time span are logged in the        combined measures set 95 (block 193).    -   (9) Exercise tolerance quality of life (QOL) measures (block        194): If the exercise tolerance QOL has decreased over 1.0 SD        from the mean exercise tolerance in the reference baseline 26        (block 195), the decrease in exercise tolerance and the time        span over which it occurs are logged in the combined measures        set 95 (block 196).    -   (10) Respiratory distress quality of life (QOL) measures (block        197): If the respiratory distress QOL measure has deteriorated        by more than 1.0 SD from the mean respiratory distress QOL        measure in the reference baseline 26 (block 198), the increase        in respiratory distress and the time span over which it occurs        are logged in the combined measures set 95 (block 199).    -   (11) Atrial fibrillation (block 200): The presence or absence of        atrial fibrillation (AF) is determined and, if present (block        201), atrial fibrillation is logged (block 202).    -   (12) Rhythm changes (block 203): The type and sequence of rhythm        changes is significant and is determined based on the timing of        the relevant rhythm measure, such as sinus rhythm. For instance,        a finding that a rhythm change to atrial fibrillation        precipitated circulatory measures changes can indicate therapy        directions against atrial fibrillation rather than primary        progression of congestive heart failure. Thus, if there are        rhythm changes (block 204), the sequence of the rhythm changes        and time span are logged (block 205).

Note also that an inversion of the indicator thresholds 129 definedabove could similarly be used for detecting a trend in diseaseregression. One skilled in the art would recognize that these measureswould vary based on whether or not they were recorded during rest orduring activity and that the measured activity score can be used toindicate the degree of patient rest or activity. The patient activityscore can be determined via an implantable motion detector, for example,as described in U.S. Pat. No. 4,428,378, issued Jan. 31, 1984, toAnderson et al., the disclosure of which is incorporated herein byreference.

The indicator thresholds 129 for detecting a trend towards a state ofcongestive heart failure can also be used to declare, a priori,congestive heart failure present, regardless of pre-existing trend datawhen certain limits are established, such as:

-   -   (1) An absolute limit of PADP (block 170) exceeding 25 mm Hg is        an a priori definition of congestive heart failure from left        ventricular volume overload.    -   (2) An absolute limit of indexed cardiac output (block 191)        falling below 2.0 l/min/m² is an a priori definition of        congestive heart failure from left ventricular myocardial pump        failure when recorded in the absence of intravascular volume        depletion (e.g., from hemorrhage, septic shock, dehydration,        etc.) as indicated by a reduced PADP (e.g., <10 mmHg).

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression and status quo of congestive heart failure 140for use in the method of FIGS. 8A and 8B. The purpose of this routine isto evaluate the presence of sufficient indicia to warrant a diagnosis ofthe onset, progression, regression, and status quo of congestive heartfailure. Quality of life and symptom measures set 25 a, 25 b can beincluded in the evaluation (block 230) by determining whether any of theindividual quality of life and symptom measures set 25 a, 25 b havechanged relative to the previously collected quality of life and symptommeasures from the monitoring sets 27 and the reference baseline 26, ifused. For example, an increase in the shortness of breath measure 87 andexercise tolerance measure 89 would corroborate a finding of congestiveheart failure. Similarly, a transition from NYHA Class II to NYHA ClassIII would indicate deterioration or, conversely, a transition from NYHAClass III to NYHA Class II status would indicate improvement orprogress. Incorporating the quality of life and symptom measures set 25a, 25 b into the evaluation can help, in part, to refute or supportfindings based on physiological data. Next, a determination as towhether any changes to interventive measures are appropriate based onthreshold stickiness (“hysteresis”) is made (block 231), as furtherdescribed below with reference to FIG. 16.

The routine returns upon either the determination of a finding orelimination of all factors as follows. If a finding of congestive heartfailure was not previously diagnosed (block 232), a determination ofdisease onset is made (block 233), as further described below withreference to FIGS. 13A-13C. Otherwise, if congestive heart failure waspreviously diagnosed (block 232), a further determination of eitherdisease progression or worsening (block 234) or regression or improving(block 235) is made, as further described below with reference to FIGS.14A-14C and 15A-15C, respectively. If, upon evaluation, neither diseaseonset (block 233), worsening (block 234) or improving (block 235) isindicated, a finding of status quo is appropriate (block 236) and noted(block 235). Otherwise, congestive heart failure and the relatedoutcomes are actively managed (block 238) through the administration of,non-exclusively, preload reduction, afterload reduction, diuresis,beta-blockade, inotropic agents, electrolyte management, electricaltherapies, mechanical therapies, and other therapies as are known in theart. The management of congestive heart failure is described, by way ofexample, in E. Braunwald, ed., “Heart Disease—A Textbook ofCardiovascular Medicine,” Ch. 17, W.B. Saunders Co. (1997), thedisclosure of which is incorporated herein by reference. The routinethen returns.

FIGS. 13A-13B are flow diagrams showing the routine for determining anonset of congestive heart failure 232 for use in the routine of FIG. 12.Congestive heart failure is possible based on two general symptomcategories: reduced exercise capacity (block 244) and respiratorydistress (block 250). An effort is made to diagnose congestive heartfailure manifesting primarily as resulting in reduced exercise capacity(block 244) and/or increased respiratory distress (block 250). Severalfactors need be indicated to warrant a diagnosis of congestive heartfailure onset, as well as progression, as summarized below withreference to FIGS. 13A-13B in TABLE 1, Disease Onset or Progression.Reduced exercise capacity generally serves as a marker of low cardiacoutput and respiratory distress as a marker of increased leftventricular end diastolic pressure. The clinical aspects of congestiveheart failure are described, by way of example, in E. Braunwald, ed.,“Heart Disease—A Textbook of Cardiovascular Medicine,” Chs. 1 and 15,W.B. Saunders Co. (1997), the disclosure of which is incorporated hereinby reference.

Per TABLE 1, multiple individual indications (blocks 240-243, 245-250)should be present for the two principal findings of congestive heartfailure related reduced exercise capacity (block 244), or congestiveheart failure related respiratory distress (block 250), to be indicated,both for disease onset or progression. A bold “++” symbol indicates aprimary key finding which is highly indicative of congestive heartfailure, that is, reduced exercise capacity or respiratory distress, abold “+” symbol indicates a secondary key finding which is stronglysuggestive, and a “±” symbol indicates a tertiary permissive findingwhich may be present or absent. The presence of primary key findingsalone can be sufficient to indicate an onset of congestive heart failureand secondary key findings serve to corroborate disease onset. Note thepresence of any abnormality can trigger an analysis for the presence orabsence of secondary disease processes, such as the presence of atrialfibrillation or pneumonia. Secondary disease considerations can beevaluated using the same indications (see, e.g., blocks 141-144 of FIGS.8A-8B), but with adjusted indicator thresholds 129 (shown in FIG. 5)triggered at a change of 0.5 SD, for example, instead of 1.0 SD.

In the described embodiment, the reduced exercise capacity andrespiratory distress findings (blocks 244, 250) can be established byconsolidating the individual indications (blocks 240-243, 245-250) inseveral ways. First, in a preferred embodiment, each individualindication (blocks 240-243, 245-250) is assigned a scaled index valuecorrelating with the relative severity of the indication. For example,decreased cardiac output (block 240) could be measured on a scale from‘1’ to ‘5’ wherein a score of ‘1’ indicates no change in cardiac outputfrom the reference point, a score of ‘2’ indicates a change exceeding0.5 SD, a score of ‘3’ indicates a change exceeding 1.0 SD, a score of‘4’ indicates a change exceeding 2.0 SD, and a score of ‘5’ indicates achange exceeding 3.0 SD. The index value for each of the individualindications (blocks 240-243, 245-250) can then either be aggregated oraveraged with a result exceeding the aggregate or average maximumindicating an appropriate congestive heart failure finding.

Preferably, all scores are weighted depending upon the assignments madefrom the measures in the reference baseline 26. For instance,transthoracic impedance 104 (shown in FIG. 4) could be weighted moreimportantly than respiratory rate 103 if the respiratory rate in thereference baseline 26 is particularly high at the outset, making thedetection of further disease progression from increases in respiratoryrate, less sensitive. In the described embodiment, cardiac output 100receives the most weight in determining a reduced exercise capacityfinding whereas pulmonary artery diastolic pressure 99 receives the mostweight in determining a respiratory distress or dyspnea finding.

Alternatively, a simple binary decision tree can be utilized whereineach of the individual indications (blocks 240-243, 245-250) is eitherpresent or is not present. All or a majority of the individualindications (blocks 240-243, 245-250) should be present for the relevantcongestive heart failure finding to be affirmed.

Other forms of consolidating the individual indications (blocks 240-243,245-250) are feasible.

FIGS. 14A-14B are flow diagrams showing the routine for determining aprogression or worsening of congestive heart failure 233 for use in theroutine of FIG. 12. The primary difference between the determinations ofdisease onset, as described with reference to FIGS. 13A-13B, and diseaseprogression is the evaluation of changes indicated in the same factorspresent in a disease onset finding. Thus, a revised congestive heartfailure finding is possible based on the same two general symptomcategories: reduced exercise capacity (block 264) and respiratorydistress (block 271). The same factors which need be indicated towarrant a diagnosis of congestive heart failure onset are evaluated todetermine disease progression, as summarized below with reference toFIGS. 14A-14B in TABLE 1, Disease Onset or Progression.

Similarly, these same factors trending in opposite directions fromdisease onset or progression, are evaluated to determine diseaseregression or improving, as summarized below with reference to FIGS.15A-15B in TABLE 2, Disease Regression. Per TABLE 2, multiple individualindications (blocks 260-263, 265-270) should be present for the twoprincipal findings of congestive heart failure related reduced exercisecapacity (block 264), or congestive heart failure related respiratorydistress (block 271), to indicate disease regression. As in TABLE 1, abold “++” symbol indicates a primary key finding which is highlyindicative of congestive heart failure, that is, reduced exercisecapacity or respiratory distress, a bold “+” symbol indicates asecondary key finding which is strongly suggestive, and a “±” symbolindicates a tertiary permissive finding which may be present or absent.The more favorable the measure, the more likely regression of congestiveheart failure is indicated.

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) 231 for use in the method of FIG. 12.Stickiness, also known as hysteresis, is a medical practice doctrinewhereby a diagnosis or therapy will not be changed based upon small ortemporary changes in a patient reading, even though those changes mighttemporarily move into a new zone of concern. For example, if a patientmeasure can vary along a scale of ‘2’ to ‘10’ with ‘10’ being worse, atransient reading of ‘6,’ standing alone, on a patient who hasconsistently indicated a reading of ‘5’ for weeks will not warrant achange in diagnosis without a definitive prolonged deterioration firstbeing indicated. Stickiness dictates that small or temporary changesrequire more diagnostic certainty, as confirmed by the persistence ofthe changes, than large changes would require for any of the monitored(device) measures. Stickiness also makes reversal of importantdiagnostic decisions, particularly those regarding life-threateningdisorders, more difficult than reversal of diagnoses of modest import.As an example, automatic external defibrillators (AEDs) manufactured byHeartstream, a subsidiary of Agilent Technologies, Seattle, Wash.,monitor heart rhythms and provide interventive shock treatment for thediagnosis of ventricular fibrillation. Once diagnosis of ventricularfibrillation and a decision to shock the patient has been made, apattern of no ventricular fibrillation must be indicated for arelatively prolonged period before the AED changes to a “no-shock”decision. As implemented in this AED example, stickiness mandatescertainty before a decision to shock is disregarded.

In practice, stickiness also dictates that acute deteriorations indisease state are treated aggressively while chronic, more slowlyprogressing disease states are treated in a more tempered fashion. Thus,if the patient status indicates a status quo (block 330), no changes intreatment or diagnosis are indicated and the routine returns. Otherwise,if the patient status indicates a change away from status quo (block330), the relative quantum of change and the length of time over whichthe change has occurred is determinative. If the change of approximately0.5 SD has occurred over the course of about one month (block 331), agradually deteriorating condition exists (block 332) and a very tempereddiagnostic, and if appropriate, treatment program is undertaken. If thechange of approximately 1.0 SD has occurred over the course of about oneweek (block 333), a more rapidly deteriorating condition exists (block334) and a slightly more aggressive diagnostic, and if appropriate,treatment program is undertaken. If the change of approximately 2.0 SDhas occurred over the course of about one day (block 335), an urgentlydeteriorating condition exists (block 336) and a moderately aggressivediagnostic, and if appropriate, treatment program is undertaken. If thechange of approximately 3.0 SD has occurred over the course of about onehour (block 337), an emergency condition exists (block 338) and animmediate diagnostic, and if appropriate, treatment program isundertaken as is practical. Finally, if the change and duration falloutside the aforementioned ranges (blocks 331-338), an exceptionalcondition exists (block 339) and the changes are reviewed manually, ifnecessary. The routine then returns. These threshold limits and timeranges may then be adapted depending upon patient history and peer-groupguidelines.

The form of the revised treatment program depends on the extent to whichthe time span between changes in the device measures exceed thethreshold stickiness 133 (shown in FIG. 5) relating to that particulartype of device measure. For example, threshold stickiness 133 indicatorfor monitoring a change in heart rate in a chronic patient sufferingfrom congestive heart failure might be 10% over a week. Consequently, achange in average heart rate 96 (shown in FIG. 4) from 80 bpm to 95 bpmover a seven day period, where a 14 beat per minute average change wouldequate to a 1.0 SD change, would exceed the threshold stickiness 133 andwould warrant a revised medical diagnosis perhaps of diseaseprogression. One skilled in the art would recognize the indications ofacute versus chronic disorders which will vary upon the type of disease,patient health status, disease indicators, length of illness, and timingof previously undertaken interventive measures, plus other factors.

The present invention provides several benefits. One benefit is improvedpredictive accuracy from the outset of patient care when a referencebaseline is incorporated into the automated diagnosis. Another benefitis an expanded knowledge base created by expanding the methodologiesapplied to a single patient to include patient peer groups and theoverall patient population. Collaterally, the information maintained inthe database could also be utilized for the development of furtherpredictive techniques and for medical research purposes. Yet a furtherbenefit is the ability to hone and improve the predictive techniquesemployed through a continual reassessment of patient therapy outcomesand morbidity rates.

Other benefits include an automated, expert system approach to thecross-referral, consideration, and potential finding or elimination ofother diseases and health disorders with similar or related etiologicalindicators and for those other disorders that may have an impact oncongestive heart failure. Although disease specific markers will provevery useful in discriminating the underlying cause of symptoms, manydiseases, other than congestive heart failure, will alter some of thesame physiological measures indicative of congestive heart failure.Consequently, an important aspect of considering the potential impact ofother disorders will be, not only the monitoring of disease specificmarkers, but the sequencing of change and the temporal evolution of moregeneral physiological measures, for example respiratory rate, arterialoxygenation, and cardiac output, to reflect disease onset, progressionor regression in more than one type of disease process.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention. TABLE 1 Disease Onset or Progression. Congestive CongestiveHeart Heart Failure Failure (Increasing (Reduced Exercise RespiratoryCapacity) Finding Distress) Finding Individual Indications (block 244,274) (block 250, 280) Decreased cardiac output ++ ± (blocks 240, 260)Decreased mixed venous + ± oxygen score (blocks 241, 261) Decreasedpatient activity + ± score (block 243, 263) Increased pulmonary artery ±++ diastolic pressure (PADP) (block 245, 265) Increased respiratory rate± + (block 246, 266) Decreased transthoracic ± + impedance (TTZ) (block248, 268)

TABLE 2 Disease Regression. Congestive Congestive Heart Heart FailureFailure (Decreasing (Improving Exercise Respiratory Capacity) FindingDistress) Finding Individual Indications (block 304) (block 310)Increased cardiac output ++ ± (block 300) Increased mixed venous + ±oxygen score (block 301) Increased patient activity + ± score (block303) Decreased pulmonary ± ++ artery diastolic pressure (PADP) (block305) Decreased respiratory rate ± + (block 306) Increased transthoracic± + impedance (TTZ) (block 308)

1. A system for diagnosing and monitoring congestive heart failure forautomated remote patient care, comprising: a database storing aplurality of monitoring sets which each comprise recorded measuresrelating to patient information recorded on a substantially continuousbasis; a server retrieving and processing a plurality of the monitoringsets, comprising: a comparison module determining at least one patientstatus change by comparing at least one recorded measure from one of themonitoring sets to at least one other recorded measure from another ofthe monitoring sets with both recorded measures relating to a type ofpatient information; and an analysis module testing each patient statuschange for one of an onset, a progression, a regression, and a statusque of congestive heart failure against a predetermined indicatorthreshold corresponding to a type of patient information as the recordedmeasures which were compared, the indicator threshold corresponding to aquantifiable physiological measure of a pathophysiology indicative ofcongestive heart failure.